Jeffrey A. Gibbons, PhD.

Advisor:

Dr. Charles P. Thompson

Dissertation Title

Can affect ratings improve identification beyond recognition?

Dissertation Abstract

The present research showed improved accuracy of identification for both "old" (previously exposed) and "new" (not previously exposed) words by combining pleasantness ratings with recognition judgments. Recognition judgments were systematically separated from pleasantness ratings across experiments because past research showed that explicit measures (e.g., recognition) may guide implicit measures (e.g., pleasantness). Similarly, words were separated into low-, medium-, and high-frequency categories because past research suggested that manipulating the exposure of "ordinary" and "novel" stimuli may influence affect ratings differently. A pleasantness deviation score accounted for individual differences in pleasantness ratings for raters and stimulus words. Recognition judgments, pleasantness ratings, and pleasantness deviation scores were combined to create an adjusted recognition measure in all three experiments. Adjusted recognition was more accurate than initial recognition in Experiments 2 and 3 when initial recognition yielded low accuracy (e.g., new words and high-frequency words). Therefore, combining multiple measures of exposure may be a successful method for improving identification.